Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 26, issue 7
Articles | Volume 26, issue 7
https://doi.org/10.5194/acp-26-4771-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-26-4771-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 26, issue 7
Technical note
 | 
10 Apr 2026
Technical note |  | 10 Apr 2026

Technical note: Hybrid machine learning model for bias correction of UTLS relative humidity against IAGOS observations in ERA5 reanalysis

Mathieu Antonopoulos, Jérémie Juvin-Quarroz, and Olivier Boucher

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Short summary
Aviation impacts climate by forming contrails that trap heat and can persist for hours at cruising altitudes. Forecasting these humid regions is difficult, as satellites lack accuracy, aircraft data are limited, and ERA5 reanalysis has random errors. This study presents a hybrid machine learning method that corrects ERA5 with aircraft data, using decision trees in dry air and neural networks in humid air. It improves relative humidity predictions, especially in the lower stratosphere.
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